{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类别名字</th>\n",
       "      <th>文本数据</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>娱乐</td>\n",
       "      <td>黎明喜当爹满面春风，乐基儿发福成大妈！离婚后两人差距这么大？</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>旅游</td>\n",
       "      <td>亚洲排名第一都市，北京GDP不及它一半，上海10年都赶超不了</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>国际</td>\n",
       "      <td>悠仁：日本明仁天皇孙辈唯一男丁堪称“独苗”，将来可能继位</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>娱乐</td>\n",
       "      <td>被意大利炮干掉的阿部规秀到底是个什么官？</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>财经</td>\n",
       "      <td>公司亏损时，股东如何维护自己的利益？</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  类别名字                            文本数据\n",
       "0   娱乐  黎明喜当爹满面春风，乐基儿发福成大妈！离婚后两人差距这么大？\n",
       "1   旅游  亚洲排名第一都市，北京GDP不及它一半，上海10年都赶超不了\n",
       "2   国际    悠仁：日本明仁天皇孙辈唯一男丁堪称“独苗”，将来可能继位\n",
       "3   娱乐            被意大利炮干掉的阿部规秀到底是个什么官？\n",
       "4   财经              公司亏损时，股东如何维护自己的利益？"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import gensim\n",
    "from gensim.models import Word2Vec\n",
    "import matplotlib.pyplot as plt\n",
    "import jieba\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "mpl.rcParams['font.serif'] = ['SimHei']\n",
    "\n",
    "data_path = \"../data/data.csv\"\n",
    "stopword_path = \"../data/stop_words.txt\"\n",
    "data = pd.read_csv(data_path)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20f9dda0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zu/anaconda3/lib/python3.7/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_groupby = data.groupby([\"类别名字\"])[\"类别名字\"].count()\n",
    "data_groupby.plot.bar(stacked = True,figsize=(20,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 词性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.iloc[:2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类别名字</th>\n",
       "      <th>文本数据</th>\n",
       "      <th>分词后的数据</th>\n",
       "      <th>词性信息</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>娱乐</td>\n",
       "      <td>黎明喜当爹满面春风，乐基儿发福成大妈！离婚后两人差距这么大？</td>\n",
       "      <td>[黎明, 喜当爹, 满面春风, 乐基儿, 发福, 成, 大妈, 离婚, 两人, 差距, 大]</td>\n",
       "      <td>[nr, v, n, nz, nr, vn, n, n, v, m, n, n, a]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>旅游</td>\n",
       "      <td>亚洲排名第一都市，北京GDP不及它一半，上海10年都赶超不了</td>\n",
       "      <td>[亚洲, 排名, 第一, 都市, 北京, GDP, 不及, 它, 一半, 上海, 10, 年...</td>\n",
       "      <td>[ns, v, m, ns, ns, eng, c, r, m, ns, m, m, d, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>国际</td>\n",
       "      <td>悠仁：日本明仁天皇孙辈唯一男丁堪称“独苗”，将来可能继位</td>\n",
       "      <td>[悠仁, 日本, 明, 仁天皇, 孙辈, 唯一, 男丁, 堪称, 独苗, 将来, 可能, 继位]</td>\n",
       "      <td>[a, ns, a, nr, nr, n, b, n, v, n, t, v, v]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>娱乐</td>\n",
       "      <td>被意大利炮干掉的阿部规秀到底是个什么官？</td>\n",
       "      <td>[被, 意大利, 炮, 干掉, 阿部规秀, 到底, 是, 个, 什么, 官]</td>\n",
       "      <td>[p, ns, n, v, nr, d, v, q, r, n]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>财经</td>\n",
       "      <td>公司亏损时，股东如何维护自己的利益？</td>\n",
       "      <td>[公司, 亏损, 时, 股东, 如何, 维护, 自己, 利益]</td>\n",
       "      <td>[n, vn, n, n, r, v, r, n]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  类别名字                            文本数据  \\\n",
       "0   娱乐  黎明喜当爹满面春风，乐基儿发福成大妈！离婚后两人差距这么大？   \n",
       "1   旅游  亚洲排名第一都市，北京GDP不及它一半，上海10年都赶超不了   \n",
       "2   国际    悠仁：日本明仁天皇孙辈唯一男丁堪称“独苗”，将来可能继位   \n",
       "3   娱乐            被意大利炮干掉的阿部规秀到底是个什么官？   \n",
       "4   财经              公司亏损时，股东如何维护自己的利益？   \n",
       "\n",
       "                                              分词后的数据  \\\n",
       "0     [黎明, 喜当爹, 满面春风, 乐基儿, 发福, 成, 大妈, 离婚, 两人, 差距, 大]   \n",
       "1  [亚洲, 排名, 第一, 都市, 北京, GDP, 不及, 它, 一半, 上海, 10, 年...   \n",
       "2   [悠仁, 日本, 明, 仁天皇, 孙辈, 唯一, 男丁, 堪称, 独苗, 将来, 可能, 继位]   \n",
       "3             [被, 意大利, 炮, 干掉, 阿部规秀, 到底, 是, 个, 什么, 官]   \n",
       "4                    [公司, 亏损, 时, 股东, 如何, 维护, 自己, 利益]   \n",
       "\n",
       "                                                词性信息  \n",
       "0        [nr, v, n, nz, nr, vn, n, n, v, m, n, n, a]  \n",
       "1  [ns, v, m, ns, ns, eng, c, r, m, ns, m, m, d, ...  \n",
       "2         [a, ns, a, nr, nr, n, b, n, v, n, t, v, v]  \n",
       "3                   [p, ns, n, v, nr, d, v, q, r, n]  \n",
       "4                          [n, vn, n, n, r, v, r, n]  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import jieba.posseg as pseg\n",
    "\n",
    "with open(stopword_path,\"r\",encoding=\"utf-8\") as f:\n",
    "    stopwords = f.readlines()\n",
    "stopwords = [i.strip() for i in stopwords]\n",
    "\n",
    "def get_pos(data):\n",
    "    a = pseg.cut(data)\n",
    "    temp = []\n",
    "    for i in a:\n",
    "        if i.word not in stopwords:\n",
    "            temp.append(i.flag)\n",
    "    return temp\n",
    "def seg_data(data):\n",
    "    a = jieba.cut(data)\n",
    "    a = [i for i in a if i not in stopwords]\n",
    "    return list(a)\n",
    "data[\"分词后的数据\"] = data[\"文本数据\"].apply(seg_data)\n",
    "data[\"词性信息\"] = data[\"文本数据\"].apply(get_pos)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_noun(data):\n",
    "    num = 0\n",
    "    for i in data:\n",
    "        if \"n\" in i:\n",
    "            num += 1\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_verb(data):\n",
    "    num = 0\n",
    "    for i in data:\n",
    "        if \"v\" in i:\n",
    "            num += 1\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_adj(data):\n",
    "    num = 0\n",
    "    for i in data:\n",
    "        if \"a\" in i:\n",
    "            num += 1\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类别名字</th>\n",
       "      <th>文本数据</th>\n",
       "      <th>分词后的数据</th>\n",
       "      <th>词性信息</th>\n",
       "      <th>名词数量</th>\n",
       "      <th>动词数量</th>\n",
       "      <th>形容词数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>娱乐</td>\n",
       "      <td>黎明喜当爹满面春风，乐基儿发福成大妈！离婚后两人差距这么大？</td>\n",
       "      <td>[黎明, 喜当爹, 满面春风, 乐基儿, 发福, 成, 大妈, 离婚, 两人, 差距, 大]</td>\n",
       "      <td>[nr, v, n, nz, nr, vn, n, n, v, m, n, n, a]</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>旅游</td>\n",
       "      <td>亚洲排名第一都市，北京GDP不及它一半，上海10年都赶超不了</td>\n",
       "      <td>[亚洲, 排名, 第一, 都市, 北京, GDP, 不及, 它, 一半, 上海, 10, 年...</td>\n",
       "      <td>[ns, v, m, ns, ns, eng, c, r, m, ns, m, m, d, ...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>国际</td>\n",
       "      <td>悠仁：日本明仁天皇孙辈唯一男丁堪称“独苗”，将来可能继位</td>\n",
       "      <td>[悠仁, 日本, 明, 仁天皇, 孙辈, 唯一, 男丁, 堪称, 独苗, 将来, 可能, 继位]</td>\n",
       "      <td>[a, ns, a, nr, nr, n, b, n, v, n, t, v, v]</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>娱乐</td>\n",
       "      <td>被意大利炮干掉的阿部规秀到底是个什么官？</td>\n",
       "      <td>[被, 意大利, 炮, 干掉, 阿部规秀, 到底, 是, 个, 什么, 官]</td>\n",
       "      <td>[p, ns, n, v, nr, d, v, q, r, n]</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>财经</td>\n",
       "      <td>公司亏损时，股东如何维护自己的利益？</td>\n",
       "      <td>[公司, 亏损, 时, 股东, 如何, 维护, 自己, 利益]</td>\n",
       "      <td>[n, vn, n, n, r, v, r, n]</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  类别名字                            文本数据  \\\n",
       "0   娱乐  黎明喜当爹满面春风，乐基儿发福成大妈！离婚后两人差距这么大？   \n",
       "1   旅游  亚洲排名第一都市，北京GDP不及它一半，上海10年都赶超不了   \n",
       "2   国际    悠仁：日本明仁天皇孙辈唯一男丁堪称“独苗”，将来可能继位   \n",
       "3   娱乐            被意大利炮干掉的阿部规秀到底是个什么官？   \n",
       "4   财经              公司亏损时，股东如何维护自己的利益？   \n",
       "\n",
       "                                              分词后的数据  \\\n",
       "0     [黎明, 喜当爹, 满面春风, 乐基儿, 发福, 成, 大妈, 离婚, 两人, 差距, 大]   \n",
       "1  [亚洲, 排名, 第一, 都市, 北京, GDP, 不及, 它, 一半, 上海, 10, 年...   \n",
       "2   [悠仁, 日本, 明, 仁天皇, 孙辈, 唯一, 男丁, 堪称, 独苗, 将来, 可能, 继位]   \n",
       "3             [被, 意大利, 炮, 干掉, 阿部规秀, 到底, 是, 个, 什么, 官]   \n",
       "4                    [公司, 亏损, 时, 股东, 如何, 维护, 自己, 利益]   \n",
       "\n",
       "                                                词性信息  名词数量  动词数量  形容词数量  \n",
       "0        [nr, v, n, nz, nr, vn, n, n, v, m, n, n, a]     9     3      1  \n",
       "1  [ns, v, m, ns, ns, eng, c, r, m, ns, m, m, d, ...     5     3      0  \n",
       "2         [a, ns, a, nr, nr, n, b, n, v, n, t, v, v]     6     3      2  \n",
       "3                   [p, ns, n, v, nr, d, v, q, r, n]     4     2      0  \n",
       "4                          [n, vn, n, n, r, v, r, n]     5     2      0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"名词数量\"] = data[\"词性信息\"].apply(count_noun)\n",
    "data[\"动词数量\"] = data[\"词性信息\"].apply(count_verb)\n",
    "data[\"形容词数量\"] = data[\"词性信息\"].apply(count_adj)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[[\"名词数量\",\"动词数量\",\"形容词数量\"]].hist(bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x106ca9160>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_groupby = data.groupby([\"类别名字\"])[\"名词数量\"].mean()\n",
    "data_groupby.plot.bar(stacked = True,figsize=(20,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a26fb8780>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_groupby = data.groupby([\"类别名字\"])[\"动词数量\"].mean()\n",
    "data_groupby.plot.bar(stacked = True,figsize=(20,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2b9120f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_groupby = data.groupby([\"类别名字\"])[\"形容词数量\"].mean()\n",
    "data_groupby.plot.bar(stacked = True,figsize=(20,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 长度分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类别名字</th>\n",
       "      <th>文本数据</th>\n",
       "      <th>分词后的数据</th>\n",
       "      <th>词性信息</th>\n",
       "      <th>名词数量</th>\n",
       "      <th>动词数量</th>\n",
       "      <th>形容词数量</th>\n",
       "      <th>长度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>娱乐</td>\n",
       "      <td>黎明喜当爹满面春风，乐基儿发福成大妈！离婚后两人差距这么大？</td>\n",
       "      <td>[黎明, 喜当爹, 满面春风, 乐基儿, 发福, 成, 大妈, 离婚, 两人, 差距, 大]</td>\n",
       "      <td>[nr, v, n, nz, nr, vn, n, n, v, m, n, n, a]</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>旅游</td>\n",
       "      <td>亚洲排名第一都市，北京GDP不及它一半，上海10年都赶超不了</td>\n",
       "      <td>[亚洲, 排名, 第一, 都市, 北京, GDP, 不及, 它, 一半, 上海, 10, 年...</td>\n",
       "      <td>[ns, v, m, ns, ns, eng, c, r, m, ns, m, m, d, ...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>国际</td>\n",
       "      <td>悠仁：日本明仁天皇孙辈唯一男丁堪称“独苗”，将来可能继位</td>\n",
       "      <td>[悠仁, 日本, 明, 仁天皇, 孙辈, 唯一, 男丁, 堪称, 独苗, 将来, 可能, 继位]</td>\n",
       "      <td>[a, ns, a, nr, nr, n, b, n, v, n, t, v, v]</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>娱乐</td>\n",
       "      <td>被意大利炮干掉的阿部规秀到底是个什么官？</td>\n",
       "      <td>[被, 意大利, 炮, 干掉, 阿部规秀, 到底, 是, 个, 什么, 官]</td>\n",
       "      <td>[p, ns, n, v, nr, d, v, q, r, n]</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>财经</td>\n",
       "      <td>公司亏损时，股东如何维护自己的利益？</td>\n",
       "      <td>[公司, 亏损, 时, 股东, 如何, 维护, 自己, 利益]</td>\n",
       "      <td>[n, vn, n, n, r, v, r, n]</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  类别名字                            文本数据  \\\n",
       "0   娱乐  黎明喜当爹满面春风，乐基儿发福成大妈！离婚后两人差距这么大？   \n",
       "1   旅游  亚洲排名第一都市，北京GDP不及它一半，上海10年都赶超不了   \n",
       "2   国际    悠仁：日本明仁天皇孙辈唯一男丁堪称“独苗”，将来可能继位   \n",
       "3   娱乐            被意大利炮干掉的阿部规秀到底是个什么官？   \n",
       "4   财经              公司亏损时，股东如何维护自己的利益？   \n",
       "\n",
       "                                              分词后的数据  \\\n",
       "0     [黎明, 喜当爹, 满面春风, 乐基儿, 发福, 成, 大妈, 离婚, 两人, 差距, 大]   \n",
       "1  [亚洲, 排名, 第一, 都市, 北京, GDP, 不及, 它, 一半, 上海, 10, 年...   \n",
       "2   [悠仁, 日本, 明, 仁天皇, 孙辈, 唯一, 男丁, 堪称, 独苗, 将来, 可能, 继位]   \n",
       "3             [被, 意大利, 炮, 干掉, 阿部规秀, 到底, 是, 个, 什么, 官]   \n",
       "4                    [公司, 亏损, 时, 股东, 如何, 维护, 自己, 利益]   \n",
       "\n",
       "                                                词性信息  名词数量  动词数量  形容词数量  长度  \n",
       "0        [nr, v, n, nz, nr, vn, n, n, v, m, n, n, a]     9     3      1  11  \n",
       "1  [ns, v, m, ns, ns, eng, c, r, m, ns, m, m, d, ...     5     3      0  15  \n",
       "2         [a, ns, a, nr, nr, n, b, n, v, n, t, v, v]     6     3      2  12  \n",
       "3                   [p, ns, n, v, nr, d, v, q, r, n]     4     2      0  10  \n",
       "4                          [n, vn, n, n, r, v, r, n]     5     2      0   8  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def length(data):\n",
    "    return len(data)\n",
    "data[\"长度\"] = data[\"分词后的数据\"].apply(length)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"长度\"].hist(bins=22)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.000000\n",
       "mean       10.278000\n",
       "std         3.515664\n",
       "min         1.000000\n",
       "25%         8.000000\n",
       "50%        10.000000\n",
       "75%        13.000000\n",
       "max        22.000000\n",
       "Name: 长度, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"长度\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cc15da0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 7200x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_groupby = data.groupby([\"类别名字\",\"长度\"])[\"长度\"].count()\n",
    "data_groupby.unstack(\"长度\").plot.bar(stacked = False,figsize=(100,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(column=['长度'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2000.000000\n",
       "mean       10.278000\n",
       "std         3.515664\n",
       "min         1.000000\n",
       "25%         8.000000\n",
       "50%        10.000000\n",
       "75%        13.000000\n",
       "max        22.000000\n",
       "Name: 长度, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"长度\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
